Supporting students' learning in the domain of computer science

Abstract

Previous studies have shown that students with low knowledge understand and learn better from more cohesive texts, whereas highknowledge students have been shown to learn better from texts of lower cohesion. This study examines whether high-knowledge readers in computer science benefit from a text of low cohesion. Undergraduate students (n ¼ 65) read one of four versions of a text concerning Local Network Topologies, orthogonally varying local and global cohesion. Participants' comprehension was examined through free-recall measure, text-based, bridging-inference, elaborative-inference, problem-solving questions and a sorting task. The results indicated that high-knowledge readers benefited from the lowcohesion text. The interaction of text cohesion and knowledge was reliable for the sorting activity, for elaborative-inference and for problem-solving questions. Although high-knowledge readers performed better in text-based and in bridging-inference questions with the low-cohesion text, the interaction of text cohesion and knowledge was not reliable. The results suggest a more complex view of when and for whom textual cohesion affects comprehension and consequently learning in computer science.

Supporting Student Learning in Computer Science Education via the Adaptive Learning Environment ALMA

“…They found that readers with low and high background-knowledge benefit from a cohesive and a minimally cohesive text respectively [26]. Gasparinatou and Grigoriadou, investigated the role of text cohesion and learners' background-knowledge in the comprehension of texts in the domain of computer science [20,21]. The results are in agreement with the results of McNamara et al, and motivated the design and the development of ALMA [26].…”

“…Its design was motivated by the results of previous studies in the field of text comprehension. Gasparinatou and Grigoriadou investigated the role of text cohesion and learners' background knowledge in the comprehension of texts in the domain of computer science [20][21][22]. The results showed that high-knowledge readers benefit from a minimally cohesive text, in contrast to low-knowledge readers who learn better from a maximally cohesive text.…”

Computer Science and Programming Courses in Geography Departments in the United States

“…Although typical GIS courses involve instruction across a broad range of concepts, the domain information in computer science is considered to be more complex in terms of fundamental knowledge and comprehension (Gasparinatou and Grigoriadou 2011). Learning in computer science is complex and fraught with known barriers, including conditions of negative reinforcement (Kinnunen and Simon 2012), impersonal interactions (Barker and Garvin-Doxas 2004), and detachment and demotivation (Babin, Tricot, and Marin e 2009).…”

Learning Programming: A Model Emerging from Data

“…Thus, the choice of one or more appropriate programming course textbooks is essential to learning [36]. Reading an appropriate textbook correlates with practice [36]. As mentioned earlier, [3] noted that programming students frequently search the textbook for solutions when they encounter problems during programming sessions.…”

“…Cluster B's core is reading, specifically 'Reading the course's assigned textbooks' (R1). Thus, the choice of one or more appropriate programming course textbooks is essential to learning [36]. Reading an appropriate textbook correlates with practice [36].…”